'Based mt' presentation slideshows

Jaime Carbonell (cs.cmu/~jgc) With Vamshi Ambati and Pinar Donmez

MT and Resource Collection for Low-Density Languages: From new MT Paradigms to Proactive Learning and Crowd Sourcing. Jaime Carbonell (www.cs.cmu.edu/~jgc) With Vamshi Ambati and Pinar Donmez Language Technologies Institute Carnegie Mellon University 20 May 2010.

723 views • 61 slides

Jaime Carbonell (cs.cmu/~jgc) With Vamshi Ambati and Pinar Donmez

MT and Resource Collection for Low-Density Languages: From new MT Paradigms to Proactive Learning and Crowd Sourcing. Jaime Carbonell (www.cs.cmu.edu/~jgc) With Vamshi Ambati and Pinar Donmez Language Technologies Institute Carnegie Mellon University 20 May 2010.

614 views • 61 slides

Introduction to MT

Introduction to MT. Ling 580 Fei Xia Week 1: 1/03/06. Outline. Course overview Introduction to MT Major challenges Major approaches Evaluation of MT systems Overview of word-based SMT. Course overview. General info. Course website:

721 views • 55 slides

NICE: Native Language Interpretation and Communication Environment

NICE: Native Language Interpretation and Communication Environment. Lori Levin, Jaime Carbonell, Alon Lavie, Ralf Brown, Erik Peterson, Katharina Probst, Rodolfo Vega, Hal Daume Language Technologies Institute Carnegie Mellon University April 12, 2001. NICE.

611 views • 50 slides

Alternatives to rule-based MT: statistical and example-based MT

Alternatives to rule-based MT: statistical and example-based MT. Lecture 25/04/2005 MODL5003 Principles and applications of machine translation slides available at: http://www.comp.leeds.ac.uk/bogdan/. 1. Overview. Classification of approaches to MT

579 views • 40 slides

Machine Translation

Machine Translation. Dai Xinyu 2006-10-27. Outline. Introduction Architecture of MT Rule-Based MT vs. Data-Driven MT Evaluation of MT Development of MT MT problems in general Some Thinking about MT from recognition.

410 views • 29 slides

China Patent Information For Western Users

China Patent Information For Western Users. Huabing Liu liuhuabing@cnipr.com Intellectual Property Publishing House, SIPO. Main Topics. Chinese patent information: new challenge to us Language barriers faced by western users About machine translation Our efforts. Looking at Chinese Patent.

361 views • 22 slides

Introduction to the Language Technologies Institute

Introduction to the Language Technologies Institute. Fall, 2008 Jaime Carbonell jgc@cs.cmu.edu. School of Computer Science at Carnegie Mellon University. Computer Science Department (theory, systems) Robotics Institute (space, industry, medical)

324 views • 22 slides

Introduction to the Language Technologies Institute

Introduction to the Language Technologies Institute. Fall, 2008 Jaime Carbonell jgc@cs.cmu.edu. School of Computer Science at Carnegie Mellon University. Computer Science Department (theory, systems) Robotics Institute (space, industry, medical)

232 views • 22 slides

New Directions in Machine Translation Introduction

New Directions in Machine Translation Introduction. ??? ????? ??? / ???. Why MT Matters?. Economics Costs ? / Quality ? / Turnaround ? Many MT developers, customers, and sponsors have already invested a lot for years. Politics Multi-lingual Countries / Minority Languages

280 views • 20 slides

The Application of Machine Translation in CADAL

The Application of Machine Translation in CADAL. Huang Chen, Chen Haiying Zhejiang University Libraries, Hangzhou, China 2006. 11. 20. About the CADAL.

172 views • 17 slides

Approaches to Machine Translation

Approaches to Machine Translation. CSC 5930 Machine Translation Fall 2012 Dr. Tom Way. How humans do translation?. Learn a foreign language: Memorize word translations Learn some patterns: Exercise: Passive activity: read, listen Active activity: write, speak Translation:

323 views • 15 slides

Multi-Engine MT for Quick MT

Multi-Engine MT for Quick MT. Missing Technology for Quick MT. NICE. LingWear. Core Rapid MT. - Multi-Engine MT - Omnivorous resource usage - Pervasive Machine Learning - Novel Approaches: * Max-Entropy models * Seeded Version Space Learning * Elicitation from native informants.

214 views • 10 slides

How to Comine Paradigms for Machine Translation

133 views • 4 slides

NICE Machine Translation for Low-Density Languages

NICE Machine Translation for Low-Density Languages. Nice (Native Language Interpretation and Communication Environment) is a project for rapid development of machine translation for low and very low density languages Classification of MT by Language Density

178 views • 3 slides